Data-driven vs. physically-based streamflow prediction models

Section 1: Publication

Publication Type

Authorship

Gauch, M., Mai, J., Gharari, S., & Lin, J.

Title

Data-driven vs. physically-based streamflow prediction models

Year

2019

Publication Outlet

In Proceedings of 9th International Workshop on Climate Informatics

DOI

ISBN

ISSN

Citation

Gauch, M., Mai, J., Gharari, S., & Lin, J. (2019a). Data-driven vs. physically-based streamflow prediction models. In Proceedings of 9th International Workshop on Climate Informatics. https://cs.uwaterloo.ca/~jimmylin/publications/Gauch_etal_CI2019.pdf

Abstract

Climate change leads to more frequent and severe floods and droughts. Precise water flow forecasts for rivers and streams help mitigate damage and are direly needed. We evaluate physically-based and data- driven models on the task of streamflow prediction in the Lake Erie region: Physically-based models capture simplified representations of the physical processes that underlie streamflow, while purely data-driven models encode no such knowledge explicitly. Experiments show that data-driven approaches can provide more accurate predictions than a physically-based model, suggesting potential in hybrid approaches that combine hydrological understanding with high prediction accuracy.

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